08-ctd-cast-data

12 February 2022

CTD casts from eDNA sampling off the Sashin on 10 May, 2021.

library(tidyr)

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library(readr)

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library(stringr)

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library(lubridate)

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library(hms)

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library(dplyr)

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library(ggplot2)

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library(RColorBrewer)

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library(lubridateExtras)

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# read in all files in the reheaded directory

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# dataFiles <- lapply(Sys.glob("CTDCasts/reheaded/*.csv"), read_csv)

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# 

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# # turn each of the data frames in that list into a single data frame

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# ctd.data <- do.call("rbind", dataFiles)

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ctd.w.tides.dist <- read_csv("../data/ctdDataframe.csv") %>%

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  filter(distance != 100)

Rows: 2538 Columns: 16── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (2): ctd_sample, tide
dbl  (13): lat, long, depth_m, salinity, density, pressure_decibar, temp_c, conductivity, sp_conduct, sound_velocity, duration, id, distance
dttm  (1): time
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
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# organize the headers, etc.

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# ctd.data2 <- ctd.data %>%

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#   rename(pressure_decibar = `Pressure (Decibar)`, depth_m = `Depth (Meter)`, temp_c = `Temperature (Celsius)`, conductivity = `Conductivity (MicroSiemens per Centimeter)`, sp_conduct = `Specific conductance (MicroSiemens per Centimeter)`, salinity = `Salinity (Practical Salinity Scale)`, sound_velocity = `Sound velocity (Meters per Second)`, density = `Density (Kilograms per Cubic Meter)`, file = `% File name`, lat = `% Start latitude`, long = `% Start longitude`, duration = `% Cast duration (Seconds)`, time = `% Cast time (local)`) %>%

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#   separate(file, into = c(NA, NA, "ctd_sample"), sep = "_") %>%

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#   select(ctd_sample, time, lat, long, depth_m, salinity, density, pressure_decibar, temp_c, conductivity, sp_conduct, sound_velocity, duration)

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# 

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# 

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# # add tide info

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# ctd.w.tides <- ctd.data2 %>%

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#   mutate(tide = ifelse(hour(hms::as_hms(time)) >12, "PM_outgoing", "AM_incoming"))

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# 

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# ctd.w.tides %>%

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#   select(lat) %>%

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#   unique() %>%

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#   arrange(lat)

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Convert longitude to meters from chum pens

This will match the other plots in the series.

# meta <- read_csv("../metadata/amalga_clean_metadata.csv")

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# 

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# dist <- meta %>%

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#   filter(!is.na(distance)) %>%

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#   arrange(distance) %>%

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#   select(distance) %>%

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#   unique()

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# 

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# dist2x <- dist %>%

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#   bind_rows(dist)

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# # each cast (ctd_sample) represents one of the 80m sampling intervals

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# ctd.w.dist <- ctd.w.tides %>%

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#   arrange(time) %>%

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#   select(time) %>%

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#   unique() %>%

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#   mutate(id = row_number()) %>%

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#   bind_cols(dist2x)

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# 

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# # bind it back together

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# ctd.w.tides.dist <- ctd.w.tides %>%

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#   left_join(., ctd.w.dist)

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# QC: are the data what we think they are?

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ctd.w.tides.dist %>%

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    mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%

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  ggplot(aes(x = long, y = lat, color = tide, linetype = tide, label = ctd_sample)) +

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 # geom_line() +

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  #geom_point(size = 0.1)+

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  geom_text(size = 2.5)+

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  facet_grid(rows = vars(tide)) +

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  theme_bw()+

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   theme(

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    axis.title.x = element_text(margin = margin(t=10)),

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    axis.title.y = element_text(margin = margin(r=10))

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  ) +

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  labs(y = "Latitude",

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       x = "Longitude") +

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  scale_color_manual(values = c("firebrick1", "midnightblue"))

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ggsave("pdf_outputs/ctdSimpleLatLon.pdf")

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Output that dataframe so that I can plot the transect on the bathy map

# ctd.w.tides.dist %>%

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#   write_csv("csv_outputs/ctdDataframe.csv")

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Ultimately, turn this into a map of the transect rather than an x-y plot.

max/min surface water temperature vs. at depth

ctd.w.tides.dist %>%

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  #filter(distance == 0) %>%

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  ggplot(aes(x = temp_c, y = -1*depth_m, color = tide)) +

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  geom_point()

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ctd.w.tides.dist %>%

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  filter(depth_m > 9 & depth_m < 11) %>%

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  summarise(min(temp_c))

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ctd.w.tides.dist %>%

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  group_by(tide) %>%

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  filter(depth_m > 1) %>%

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  summarise(min(temp_c))

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Plotting CTD profiles

# salinity

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ctd.w.tides.dist %>%

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  filter(salinity > 20) %>%

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  mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%

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  ggplot(aes(x = density, y = -1*(depth_m), color = distance)) +

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  geom_point() +

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  facet_grid(rows = vars(tide)) +

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  theme_bw()+

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   theme(

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    axis.title.x = element_text(margin = margin(t=10)),

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    axis.title.y = element_text(margin = margin(r=10))

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  ) +

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  labs(y = "Depth (m)",

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       x = "Density",

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       color = "Distance")

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#ggsave("pdf_outputs/ctdSalinityProfile.pdf", width = 8, height = 5)

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Salinity: zoomed in

# temp

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ctd.w.tides.dist %>%

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    filter(salinity > 20) %>%

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  mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%

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  filter(depth_m < 10) %>% # top three meters of the surface

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  ggplot(aes(x = density, y = -1*(depth_m), color = distance)) +

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  geom_point() +

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  facet_grid(rows = vars(tide)) +

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  theme_bw()+

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   theme(

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    axis.title.x = element_text(margin = margin(t=10)),

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    axis.title.y = element_text(margin = margin(r=10))) +

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  labs(y = "Depth (m)",

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       x = "Density (kg/m^3)",

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       color = "Distance (m)")

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#ggsave("pdf_outputs/ctdSalinityProfile5m.pdf", width = 8, height = 5)

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Plotting a surface with temperature and longitude

https://github.com/cathmmitchell/plottingOceanDataWithR/wiki/Irregular-data

binned <- ctd.w.tides.dist %>% 

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    mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%

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  filter(depth_m < 10) %>%

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  mutate_at(5, round)%>% 

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  group_by(distance, tide, depth_m) %>% 

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  summarize(av_tmp = mean(temp_c))

`summarise()` has grouped output by 'distance', 'tide'. You can override using the `.groups` argument.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
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# flip order so higher temps are at the top!

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binned

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tempplot <- ggplot(binned,aes(x=distance,y=-1*(depth_m))) +

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  facet_grid(rows = vars(tide))+

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  geom_contour_filled(aes(z=av_tmp), alpha = 0.8) +

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    geom_point(size =0.1) +

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  theme_bw()+

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   theme(

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    axis.title.x = element_text(margin = margin(t=10)),

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    axis.title.y = element_text(margin = margin(r=10))) +

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  labs(y = "Depth (m)",

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       x = "Distance from hatchery pens (m)",

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       fill = "Temperature (C)") +

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  scale_x_continuous(expand = c(0,0)) +

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  scale_y_continuous(expand = c(0,0.05)) +

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  scale_fill_brewer(palette = "Spectral", direction = -1,

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                    guide = guide_legend(reverse = T))

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tempplot

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ggsave("pdf_outputs/ctdTemp10metersDistance.png", width = 8, height = 5)

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Why do some of the top depth values drop out? Something about the binning.

Do it once more, but setting the depth even shallower:

binned2 <- ctd.w.tides.dist %>% 
    mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%
  filter(depth_m < 2.5) %>%
  mutate_at(5, round, 2)%>% 
  group_by(distance, tide, depth_m) %>% 
  summarize(av_tmp = mean(temp_c))

`summarise()` has grouped output by 'distance', 'tide'. You can override using the `.groups` argument.

ggplot(binned2,aes(x=distance,y=-1*(depth_m))) +
  facet_grid(rows = vars(tide))+
  geom_contour_filled(aes(z=av_tmp), alpha = 0.8) +
  geom_point(size =0.1) +
  theme_bw()+
   theme(
    axis.title.x = element_text(margin = margin(t=10)),
    axis.title.y = element_text(margin = margin(r=10))) +
  labs(y = "Depth (m)",
       x = "Distance from hatchery pens (m)",
       fill = "Temperature (C)")

ggsave("pdf_outputs/ctdTemp3metersDistance.pdf", width = 8, height = 5)

For salinity and temp, let’s zoom in on the top 5 meters (we took samples from the first ~1 m)

# temp
ctd.w.tides.dist %>%
  mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%
  filter(depth_m < 5) %>%
  ggplot(aes(x = temp_c, y = -1*(depth_m), color = distance)) +
  geom_point() +
  facet_grid(rows = vars(tide)) +
  theme_bw()+
   theme(
    axis.title.x = element_text(margin = margin(t=10)),
    axis.title.y = element_text(margin = margin(r=10))) +
  labs(y = "Depth (m)",
       x = "Temperature (C)",
       color = "Distance (m)")

ggsave("pdf_outputs/ctdTempProfile3metersDistance.pdf", width = 8, height = 5)

Plotting a surface with salinity and longitude

binnedSalinity <- ctd.w.tides.dist %>% 

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    mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%

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  filter(depth_m < 10) %>%

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  mutate_at(5, round)%>% 

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  group_by(distance, tide, depth_m) %>% 

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  summarize(av_salinity = mean(salinity))

`summarise()` has grouped output by 'distance', 'tide'. You can override using the `.groups` argument.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
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# oranize colors

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mycolors <- colorRampPalette(brewer.pal(9, "Oranges"))(11)

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salplot <- ggplot(binnedSalinity, aes(x = distance, y = -1*(depth_m))) +

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  facet_grid(rows = vars(tide)) +

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  geom_contour_filled(aes(z = av_salinity), alpha = 0.8) +

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    geom_point(size = 0.1) +

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

    theme_bw() +

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

     theme(

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

      axis.title.x = element_text(margin = margin(t=10)),

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

      axis.title.y = element_text(margin = margin(r=10))) +

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

      labs(y = "Depth (m)",

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

           x = "Distance from hatchery pens (m)",

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

           fill = "Salinity (PSU)") +

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

  scale_x_continuous(expand = c(0,0)) +

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

  scale_y_continuous(expand = c(0,0.05))

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

salplot

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

ggsave("pdf_outputs/ctdSalinity10metersDistance.png", width = 8, height = 5)

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

binnedSal2 <- ctd.w.tides.dist %>% 
    mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%
  filter(depth_m < 2.5) %>%
  mutate_at(5, round, 2)%>% 
  group_by(distance, tide, depth_m) %>% 
  summarize(av_sal = mean(salinity))

`summarise()` has grouped output by 'distance', 'tide'. You can override using the `.groups` argument.

ggplot(binnedSal2,aes(x=distance,y=-1*(depth_m))) +
  facet_grid(rows = vars(tide))+
  geom_contour_filled(aes(z=av_sal), alpha = 0.8) +
  geom_point(size =0.1) +
  theme_bw()+
   theme(
    axis.title.x = element_text(margin = margin(t=10)),
    axis.title.y = element_text(margin = margin(r=10))) +
  labs(y = "Depth (m)",
       x = "Distance from hatchery pens (m)",
       fill = "Salinity (PSU)")

ggsave("pdf_outputs/ctdSalinity3metersDistance.png", width = 8, height = 5)

library(patchwork)

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

salplot + tempplot + plot_layout(nrow = 2) + plot_annotation(tag_levels = "A")

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

ggsave("pdf_outputs/ctd_data2021_dualplot.png", width = 8, height = 8)

Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument

ctd.w.tides.dist %>%
  group_by(tide) %>%
  summarise(max(salinity))

Density

density1 <- ctd.w.tides.dist %>% 
  mutate(tide = ifelse(tide == "AM_incoming", "incoming tide (AM)", "outgoing tide (PM)")) %>%
  filter(depth_m < 10) %>%
  mutate_at(5, round)%>% 
  group_by(distance, tide, depth_m) %>% 
  summarize(av_density = mean(density)) # the avg needs to be taken for proper plotting in this way.

`summarise()` has grouped output by 'distance', 'tide'. You can override using the `.groups` argument.

ggplot(density1,aes(x=distance,y=-1*(depth_m))) +
  facet_grid(rows = vars(tide))+
  geom_contour_filled(aes(z=density), alpha = 0.8) +
    geom_point(size =0.1) +
  theme_bw()+
   theme(
    axis.title.x = element_text(margin = margin(t=10)),
    axis.title.y = element_text(margin = margin(r=10))) +
  labs(y = "Depth (m)",
       x = "Distance from hatchery pens (m)",
       fill = "Density (kg/m^3)")

Error in `geom_contour_filled()`:
! Problem while computing aesthetics.
ℹ Error occurred in the 1st layer.
Caused by error in `compute_aesthetics()`:
! Aesthetics are not valid data columns.
✖ The following aesthetics are invalid:
✖ `z = density`
ℹ Did you mistype the name of a data column or forget to add `after_stat()`?
Backtrace:
  1. base (local) `<fn>`(x)
  2. ggplot2:::print.ggplot(x)
  4. ggplot2:::ggplot_build.ggplot(x)
  5. ggplot2:::by_layer(...)
 12. ggplot2 (local) f(l = layers[[i]], d = data[[i]])
 13. l$compute_aesthetics(d, plot)
 14. ggplot2 (local) compute_aesthetics(..., self = self)

Tide data

Tide data was obtained from the NOAA Tides website: tidesandcurrents.noaa.gov

https://tidesandcurrents.noaa.gov/waterlevels.html?id=9452210&units=standard&bdate=20210510&edate=20210511&timezone=LST/LDT&datum=MLLW&interval=6&action=

Look at joining the tide data with the ctd data based on time

# double check the sampling to see if it overlaps slack tide?
tides.hour2 %>%
  filter(Date == "2022-05-05") %>%
  left_join(., ctd.times.2022, by = c("hour", "ymd")) %>%
  filter(tide == "AM_outgoing") %>%
  mutate(sampling_period = ifelse(is.na(ctd_sample), "no", "yes")) %>%
  filter(height_ft < min(height_ft)+1) %>%
  ggplot(aes(x = long, y = lat, color = newdate)) +
  geom_point()

Warning: Detected an unexpected many-to-many relationship between `x` and `y`.

Minimum tidal height was at 10:36; the parallel transect was sampled after 11 am, so it was as the tide turned.

# make supplemental figure
tidePlot2021 + tidePlot2022 + 
  plot_layout(ncol = 1) +
  plot_annotation(tag_levels = "A")

ggsave("pdf_outputs/SIfigure_tideCycle_bothYears.png", width = 8, height = 8)